Because of individual variability in drug response, the physician is faced with a quantitative therapeutic decision as to the proper dose for any drug he prescribes. Adjusting dosage regimens becomes more difficult at times when drug elimination may be changing, as during disease states, particularly those involving the liver (drug-disease interactions) or due to changes in pharmacokinetics produced by a second drug (drug-drug interactions). The objective of the proposed project is the investigation of metabolic drug-drug and drug-disease interactions. By obtaining drug concentration/time profiles, protein binding data, and urinary metabolite excretion patterns in patients receiving particular drug combinations, or in patients with liver disease in whom hepatic drug clearance may be altered, we will evaluate the following questions: 1) On a clinical basis are dosage adjustments necessary when a patient is given a specific drug combination, or when a specific drug is used in a patient with a particular state of hepatic dysfunction. 2) If changes in drug disposition do occur as a result of drug-drug or drug-disease interactions, what is the mechanism for changes (altered protein binding; increases or decreases in hepatic clearance; changes in distribution). 3) Can mathematical models of metabolic interaction be developed which are sufficiently general to allow them to be used in a predictive manner. 4) Are there patterns of particular disease states and drug types which should alert the physician to look for toxicity or lack of efficacy. Development of predictive models is based on the premise that, like pharmacologic effects, drug-drug and drug-disease interactions are graded phenomena dependent on the concentrations of drugs at the interacting site or the type and degree of hepatic dysfunction. The proposal employs longitudinal study design which allows fewer subjects to be used and takes advantage of the existing MEDIPHOR drug interaction system to identify patients and study certain drug-drug interactions prospectively.